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3rd International Conference on Eco Engineering Development, ICEED 2019

作者： Darmalim, Usin Darmalim, Ferdinan Darmalim, Sutristo Hidayat, Alam Ahmad Budiarto, Arif Mahesworo, Bharuno Pardamean, Bens Mega Medica Pharmaceuticals Jakarta11840 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Channa striata or the striped snakehead fish is one of snakehead fish species which inhabits all types of freshwater bodies distributed across Asian countries. Because this fish is known to have higher albumin fraction (64.61%) of protein and other economic values, domestication, and cultivation of this fish has been done in many Asian countries such as Indonesia, China, Malaysia, Thailand, Bangladesh, and India. Environmental factors such as temperature, water pH, dissolved oxygen, total dissolved solids, and turbidity are important parameters must be considered inbreeding and growing this type of fish. The aim of this paper is to propose an IoT solution to automatically monitor these environmental factors. It is designed with affordable and open-source electrical components to provide a cost-efficient solution for farmers. Five sensors are used to measure each parameter. A web application prototype is also presented as a companion application for the users to get useful information from the IoT device. It is developed using a Pythonframework. By accessing this web application, the users can immediately detect any abnormal conditions of the pond. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fish

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S100A4 released from highly bone-metastatic breast cancer cells plays a critical role in osteolysis

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Bone Research 2019年第4期7卷 424-436页

作者： Haemin Kim Bongjun Kim Sang Il Kim Hyung Joon Kim Brian YRyu Junho Chung Zang Hee Lee Hong-Hee Kim Department of Cell and Developmental Biology BK21 Program and DRISeoul National UniversitySeoulKorea Department of Biochemistry and Molecular Biology Seoul National University College of MedicineSeoulKorea Cancer Research Institute Seoul National University College of MedicineSeoulKorea Department of Cancer Biology Seoul National University College of MedicineSeoulKorea Department of Oral Physiology BK21 PLUS Projectand Dental and Life Science InstituteSchool of DentistryPusan National UniversityYangsanKorea Interdisciplinary Program in Bioinformatics Seoul National University College of Natural SciencesSeoulKorea Department of Biomedical Sciences Seoul National University Graduate SchoolSeoulKorea

Bone destruction induced by breast cancer metastasis causes severe complications,including death,in breast cancer *** between cancer cells and skeletal cells in metastatic bone microenvironments is a principal element that drives tumor progression and ***-derived factors play fundamental roles in this form of *** identify soluble factors released from cancer cells in bone metastasis,we established a highly bone-metastatic subline of MDA-MB-231 breast cancer *** subline(mtMDA)showed a markedly elevated ability to secrete S100A4 protein,which directly stimulated osteoclast formation via surface receptor *** S100A4 stimulated osteoclastogenesis in vitro and bone loss in *** medium from mtMDA cells in which S100A4 was knocked down had a reduced ability to stimulate ***,the S100A4 knockdown cells elicited less bone destruction in mice than the control knockdown *** addition,administration of an anti-S100A4 monoclonal antibody(mAb)that we developed attenuated the stimulation of osteoclastogenesis and bone loss by mtMDA in *** together,our results suggest that S100A4 released from breast cancer cells is an important player in the osteolysis caused by breast cancer bone metastasis.

关键词： S100A4 breast metastatic

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Comparative analysis of deep learning models for detecting face mask 7

Comparative analysis of deep learning models for detecting f...

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7th International Conference on Computer Science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

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Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach 8

Determining Variables Associated with Annual Oil Palm Yield:...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Elwirehardja, Gregorius Natanael Suparyanto, Teddy Miftakhurrokhmat Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta55282 Indonesia Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 RRʹ. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed. © 2023 The Authors. Published by Elsevier B.V.

关键词： Machine Learning Oil Palm Yield SHAP XAI XGBoost

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P202: Post-COVID syndrome presented with psychomotor change and suicidal ideations: a geriatric case report

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International Psychogeriatrics 2023年第S1期35卷 229-230页

作者： Lin, Ying Wang, Ying-Chi Chiang, Dai-Lun Lai, Feipei Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei City Taiwan MacKay Memorial Hospital Department of Psychiatry Taipei Taiwan Financial Technology Applications Program Ming Chuan University Taoyuan City Taiwan

Background:COVID-19 is notorious for its neuropsychiatric sequelae. Some patients present with anosmia and cognitive and attention deficits, also known as “brain-fog”. In COVID-19 survivors, psychiatric manifestations such as depression, fatigue, anxiety, and neurocognitive impairments, also known as the “post-acute COVID-19 syndrome” or “long COVID”, were reported in 35% of the patients after the infection. New-onset anxiety, depression, psychosis, seizures, and even suicidal behaviors are also reported. Inflammation was proposed to be the *** Report:The 68 year-old married female who lived with her husband did not have psychiatric history before this year. She had covid-19 infection 3 months prior to her psychiatric hospitalization. Although she had recovered, she started to have depressed mood, disrupted sleep and anhedonia since then. She became easily anxious and could hardly feed her cats as usual. She was not able to make decisions. She no longer did house chores that she had been doing. Poor appetite led to a loss of body weight from 46 to 40kgs. She had blunted emotional response, and could not cry after her cat died. She went to clinic for help, BZD and antidepressants were given, but the condition persisted. One week prior to hospitalization, her speech became weak, murmuring and could not finish a long sentence. The physiological change can be observed using wearable device. After 3 weeks of treatment, her heat rate, activity and sleep improved. The progress was also recorded via wearable devices and clinical ***:In our patient, the depression was associated with circadian rhythm disruption, suicidal ideations and psychomotor slowing. This may reveal some special features of post-COVID depression. In previous studies, antidepressants have proved to be effective in treating post-COVID-19 depression. Sigma receptors are one of the possible mechanisms. Besides, agomelatine, with its melatonergic activity of regulating circadia

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Count-Data Mixed Models of Topical Tweets: A Case of Indonesia Flood Events

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Procedia Computer Science 2023年 227卷 805-812页

作者： Alam Ahmad Hidayat Bens Pardamean Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Examining topic-level variability in modeling Twitter data can potentially yield more comprehensive insights into public perception during critical periods, thereby enhancing natural disaster mitigation and surveillance efforts. In this study, we utilized generalized linear mixed models (GLMMs) to illustrate the variability in tweet counts related to specific topics in Indonesia during the flood events that occurred in February 2021. The glmmTMB library in R was employed for this purpose. The data were assumed to follow two distinct exponential distributions: Poisson and Negative Binomial. To incorporate random effects, random intercepts and random slopes were introduced, allowing them to vary randomly across topics in the initial two models. Additionally, the final model addressed issues related to dispersion and zero-inflation. By evaluating the Akaike Information Criteria scores, we determined that a model based on the Negative Binomial distribution with random zero-inflation intercepts best fit the data. The chosen model formulation and the estimated parameters have the potential to forecast topic-specific trends in Indonesian flood-related Twitter data.

关键词： count data disaster surveillance floods mixed models Twitter

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A COMPREHENSIVE SOFTWARE SUITE FOR THE ANALYSIS OF CDNAS

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Genomics, Proteomics & bioinformatics 2005年第3期3卷 179-188页

作者： KAZUHARU ARAKAWA HARUO SUZUKI KOSUKE FUJISHIMA KENJI FUJIMOTO SHO UEDA MOTOMU MATSUI MASARU TOMITA INSTITUTE FOR ADVANCED BIOSCIENCES KEIO UNIVERSITY FUJISAWA 252-8520 JAPAN BIOINFORMATICS PROGRAM OF THE GRADUATE SCHOOL OF MEDIA AND GOV- ERNANCE KEIO UNIVERSITY FUJISAWA 252-8520 JAPAN INSTITUTE FOR ADVANCED BIOSCIENCES KEIO UNIVERSITYFUJISAWA 252-8520 JAPAN

We have developed a comprehensive software suite for bioinformatics research of cDNAs; it is aimed at rapid characterization of the features of genes and the proteins they code. Methods implemented include the detection of translation initia- tion and termination signals, statistical analysis of codon usage, comparative study of amino acid composition, comparative modeling of the structures of product proteins, prediction of alternative splice forms, and metabolic pathway reconstruction. The software package is freely available under the GNU General Public License at http： / /***/ data/cdna/.

关键词： cDNA bioinformatics, software

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Flor Revisited(Again):eQTL and Mutational Analysis of NB-LRR Mediated Immunity to Powdery Mildew in Barley

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Journal of Integrative Agriculture 2014年第2期13卷 237-243页

作者： Roger Wise Priyanka Surana Greg Fuerst Ruo Xu Divya Mistry Julie Dickerson Dan Nettleton Corn Insects and Crop Genetics Research Unit U.S.Department of Agriculture-Agricultural Research ServiceIowa State UniversityAmesIowa 50011USA Department of Plant Pathology & Microbiology Center for Plant Responses to Environmental StressesIowa State UniversityAmesIowa 50011USA Bioinformatics and Computational Biology Graduate Program Iowa State UniversityAmesIowa 50011USA Department of Statistics Iowa State UniversityAmesIowa 50011USA Department of Electrical and Computer Engineering Iowa State UniversityAmesIowa 50011USA

Genes encoding early signaling events in pathogen defense often are identified only by their phenotype. Such genes involved in barley-powdery mildew interactions include Mla, specifying race-specific resistance; Rarl （Required for Mla12-specified resistance1）, and Roml （Restoration of Mla-specified resistancel）. The HSP90-SGT1-RAR1 complex appears to function as chaperone in MLA-specified resistance, however, much remains to be discovered regarding the precise signaling underlying plant immunity. Genetic analyses of fast-neutron mutants derived from CI 16151 （Mla6） uncovered a novel locus, designated Rar3 （R_equired for Mla6-specified resitance3）. Rar3 segregates independent of Mla6 and Rarl, and rar3 mutants are susceptible to Blumeria graminis f. sp. hordei （Bgh） isolate 5874 （A VRar）, whereas, wild-type progenitor plants are resistant. Comparative expression analyses of the rar3 mutant vs. its wild-type progenitor were conducted via Barleyl GeneChip and GAIIx paired-end RNA-Seq. Whereas Rarl affects transcription of relatively few genes; Rar3 appears to influence thousands, notably in genes controlling ATP binding, catalytic activity, transcription, and phosphorylation; possibly membrane bound or in the nucleus, eQTL analysis of a segregating doubled haploid population identified over two-thousand genes as being regulated by Mla （q value/FDR=0.00001）, a subset of which are significant in Rar3 interactions. The intersection of datasets derived from mla-loss-of-function mutants, Mla-associated eQTL, and rar3-mediated transcriptome reprogramming are narrowing the focus on essential genes required for Mla-specified immunity.

关键词： eQTL transcript profiling immunity resistance signaling barley Blumeria graminis

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Indonesian Agricultural-crops Classification Using Transfer Learning Model

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Procedia Computer Science 2023年 227卷 128-136页

作者： Mahmud Isnan Alam Ahmad Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Department of Mathematics School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNet- B0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues.

关键词： Agriculture Classification Crop Deep Learning Transfer learning

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Analysis of Single-Cell RNA Sequencing Data Unveils Novel Immune Prognostic Biomarkers

Analysis of Single-Cell RNA Sequencing Data Unveils Novel Im...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Wenjuan Zhang Alex Lee Mary Qu Yang Information Science Department MidSouth Bioinformatics Center and Bioinformatics Graduate Program University of Arkansas Little Rock and Univ. of Arkansas for Medical Sciences University of Arkansas Little Rock Little Rock USA Biology Department University of Arkansas Little Rock Little Rock USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

The immune system plays a central role in recognizing and eliminating cancerous cells. The activity and functionality of immune cells within the tumor microenvironment can significantly impact the prognosis of cancer patients. Leveraging single-cell RNA sequencing technology, we conducted an in-depth investigation into single-cell-based gene signatures for predicting cancer patient survival. The study utilized single-cell RNA sequencing data from breast cancer patients, encompassing over 14,875 immune cells. Gene signatures from major innate immune cell types, including Natural Killer (NK) cells, Macrophages, and Neutrophils, as well as adaptive immune cells, including Cytotoxic T Lymphocytes (T cells) and B lymphocytes (B cells), were identified. To elucidate their relationship in breast cancer patients, we constructed Cox proportional hazard regression models for predicting patient survival, utilizing the concordance index as a measure of prediction model performance. The evaluation incorporated three independent breast cancer patient cohorts: TCGA (The Cancer Genome Atlas program), METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), and UK breast cancer data. Our results showed that the T cell-based signature model outperformed the NK cell-based and Neutrophil-based prediction models. Moreover, the $T$ cell-based signature model exhibited robust performance across independent patient datasets. These findings suggest that gene signatures derived from cells of the adaptive immune system have a better survival prediction value than those derived from the innate immune system, underscoring the potential clinical relevance of harnessing the adaptive immune response for refining prognostic assessments in breast cancer. These insights contribute to advancing our understanding of the complex interplay between immune cells and cancer prognosis, paving the way for more tailored and effective therapeutic interventions.

关键词： Adaptation models Sequential analysis Adaptive systems RNA Predictive models Biomarkers Breast cancer

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